To be specific, we employ a Triplet network[1]} as our deep metric module for learning the similarity metric.
The deep metric module consists of three instances of the same feedforward network \(M\)  with shared parameters.
This module has three types of inputs, namely, the anchor \(\mathbf {x}\) , the positive sample \(\mathbf {x}^+\) , and the negative sample \(\mathbf {x}^-\) .
Here, we use clean images as anchors and obtain the positive and negative samples by feeding the clean images into the revised cGAN. When accompanied with correct labels, the output reconstructions are marked as positive samples; otherwise, they are marked as negative samples.
These three kinds of samples are fed into the deep metric module to get their respective embeddings.
